Postprint version. Published in Artificial Neural Networks for Robotics Coordinate Transformation, Volume 22, Issue 4, October 1, 1992, pages 481-493.
NOTE: At the time of publication, the author Sema Alptekin was not yet affiliated with Cal Poly.
The definitive version is available at https://doi.org/10.1016/0360-8352(92)90023-D.
Artificial neural networks with such characteristics as learning, graceful degradation, and speed inherent to parallel distributed architectures might provide a flexible and cost solution to the real time control of robotics systems. In this investigation artificial neural networks are presented for the coordinate transformation mapping of a two-axis robot modeled with Fischertechnik physical modeling components. The results indicate that artificial neural systems could be utilized for practical situations and that extended research in these neural structures could provide adaptive architectures for dynamic robotics control.
Industrial Engineering | Manufacturing
1992 Elsevier Science Ltd.